1 Field of the Invention
The present invention relates to method and apparatus for an inference using frame-based knowledge representation, primarily utilized in the artificial intelligence technology, where various knowledge on an object of interest are collectively stored in a frame, and an inference utilizes a hierarchical data structure formed by frames interactively and, more particularly, to method and apparatus for a constraint-oriented inference where a particular slot value of a slot of a frame is deduced from constraints imposed on slot values and their mutual relationships.
2. Description of the Prior Art
A method known as frame-based knowledge representation is suitable for representing knowledge on an object of interest in a flexible manner which allows easy access and management of the knowledge.
In this frame-based knowledge representation, an object is represented by a frame comprised of slot-slot value pairs. Each slot represents a categorized attribute and each slot value represents an attribute value corresponding to the object.
In such frame-based knowledge representation, if a slot value of a particular slot of a frame required was not found, or if a slot whose slot value is required does not exist in a frame, a slot value of a corresponding slot belonging to a frame representing a broader concept of the concept represented by the frame in question will be inherited as the slot value of the frame in question, or the slot value required will be deduced from prescribed rules accompanying the slot.
On the other hand, in a method known as constraint-oriented inference, constraints indicating relationship among the slot values of different frames are imposed so that a missing slot value of a frame can be derived from the constraints and other known slot values of the other frames. For example, let R(V1, V2) be a constraint imposed on a slot value V1 of a slot S1 in a frame F1 and a slot value V2 of a slot S2 in a frame F2, where the relation R(V1, V2) defines V1 in terms of V2, or vice versa. And suppose V1 is not known while V2 is known. Then if V1 of S1 in F1 was required, it will be derived from R(V1, V2) and V2 of S2 in F2. Obviously, the opposite situation can be handled by the same relation R(V1, V2).
In such constraint-oriented inference, it is necessary to provide means to make constraints accessible by slots so that constraints can be accessed by all relevant slots. For instance, in the last example, a constraint R(V1, V2) must be accessed by both S1 in F1 and S2 in F2.
Conventionally, either an operator has to make constraints accessible by slots by attaching pointers which provide paths for access to each relevant slot of given constraints, or else it is necessary to accompany each slot with a pattern for searching constraints to which it is relevant and carry out searching by pattern matching.
However, in the conventional methods there are problems such as the low efficiency in knowledge base formation and the insufficient ability to maintain consistency among slots. In particular, when many slots are relevant to one constraint, the process of making each of these slots accessible to the constraint can be quite complex, and if the need for correction arises, the process of correcting can be very cumbersome.